Search Results (1,035)

An accurate assessment of the state of health (SOH) of lithium-ion batteries (LIBs) is crucial for ensuring the safety and reliability of energy storage systems and electric vehicles. However, existing methods face challenges: physics-based models are computationally complex, traditional data-driven methods rely heavily on manual feature engineering, and single models lack the ability to capture both local and global degradation patterns. To address these issues, this paper proposes a novel hybrid LSTM–Transformer model for LIB SOH estimation using actual measurement data. The model integrates Long Short-Term Memory (LSTM) networks to capture local temporal dependencies with the Trans-former architecture to model global degradation trends through self-attention mechanisms. Experimental validation was conducted using eight 18650 Nickel Cobalt Manganese (NCM) LIBs subjected to 750 charge–discharge cycles under room temperature conditions. Sixteen statistical features were extracted from voltage and current data during constant current–constant voltage (CC-CV) phases, with feature selection based on the Pearson correlation coefficient and maximum information coefficient analysis. The proposed LSTM–Transformer model demonstrated superior performance compared to the standalone LSTM and Transformer models, achieving a mean absolute error (MAE) as low as 0.001775, root mean square error (RMSE) of 0.002147, and mean absolute percentage error (MAPE) of 0.196% for individual batteries. Core features including cumulative charge (CC Q), charging time, and voltage slope during the constant current phase showed a strong correlation with the SOH (absolute PCC > 0.8). The hybrid model exhibited excellent generalization across different battery cells with consistent error distributions and nearly overlapping prediction curves with actual SOH trajectories. The symmetrical LSTM–Transformer hybrid architecture provides an accurate, robust, and generalizable solution for LIB SOH assessment, effectively overcoming the limitations of traditional methods while offering potential for real-time battery management system applications. This approach enables health feature learning without manual feature engineering, representing an advancement in data-driven battery health monitoring. Full article

Intergenerational trauma significantly affects the health and mental health of African American families, particularly women whose lives are shaped by systemic inequities and historical oppression. This scoping review examines how trauma and resilience are transmitted across generations in African American communities, with a focus on dyads such as mother–child and mother–daughter relationships and a conceptual grandmother–mother–daughter triad. The review aims to identify mechanisms of trauma transmission and resilience and to inform culturally responsive, multigenerational interventions. Peer-reviewed studies published between 2012 and 2025 were identified that included African American caregivers and children and addressed biological, psychological, social, cultural, and resilience dimensions of intergenerational processes. Data were synthesized using a dimensional conceptual analysis approach. Findings indicate that intergenerational trauma is perpetuated through chronic stress and discrimination, maternal mental health challenges, family structure and caregiving strain, and cultural narratives about strength and self-reliance. At the same time, resilience is transmitted through sensitive caregiving, spirituality and faith, social and kin support, racial socialization, and economic survival strategies that draw on cultural and historical knowledge. These results underscore the importance of addressing intergenerational trauma holistically by integrating dyadic evidence within a broader conceptual triadic framework. Culturally responsive, multigenerational interventions that leverage family and community strengths and make space for emotional vulnerability are essential for interrupting cycles of trauma and fostering healing within African American families. Full article

Background/Objectives: Kidney donation remains a critical component of addressing end-stage renal disease. This study examines differences in awareness, willingness to donate, and concerns related to kidney donation among medical and non-medical university students. By comparing these groups within the context of Croatia’s presumed-consent system for organ donation, the study provides insights into how educational backgrounds shape attitudes in a setting with high transplantation rates but limited data on young adults. Methods: A cross-sectional observational study targeted at medical and non-medical university students in Croatia. Data were collected from 640 participants via a self-administered, close-ended, structured questionnaire with 33 items divided across three sections. Responses were analyzed using IBM SPSS Statistics program (v. 30.0), to identify significant differences. Due to the cross-sectional design, causal relationships could not be inferred. Results: Overall, 190 students (28.7%) reported willingness to donate a kidney during their lifetime, which was more common among medical students (N = 59; 39.0%) than non-medical students (N = 131; 26.8%). Collectively, willingness to donate postmortem was high in both groups (N = 527; 82.3%), as was willingness in a brain-dead state (N = 448; 70.0%). Medical and non-medical students mostly cited perceived health risks as a concern and concerns related to surgical complications. Regarding information sources, 33.2% of students reported inadequate knowledge of kidney donation, with social media and internet searches cited more frequently than healthcare professionals. Conclusions: Our findings indicate that medical and non-medical students exhibit distinct gaps in knowledge, risk perception and willingness toward kidney donation. Within Croatia’s presumed-consent framework, these findings highlight the importance of targeted educational strategies to support informed decision-making among future generations. Full article

Background/Objectives: Socioeconomic stressors, such as financial strain, rising living costs, and perceived price burden, have gained relevance in the post-pandemic period and may adversely affect adolescent mental health. This study examined the association between subjective financial stress and symptoms of depression, anxiety, and emotional/behavioral difficulties among adolescents in Northern Italy. Methods: Data were obtained from the 2025 Corona and Psyche South Tyrol (COP-S) population survey. A total of 2554 adolescents aged 11–19 years and their parents participated; 1598 adolescents provided complete data for analyses of socioeconomic stressors (parent-reported Family Affluence Scale III, adolescent self-reported and parent proxy and self-reported burden due to price increases). Mental health outcomes included depressive symptoms (PHQ-2), generalized anxiety (SCARED-GAD), and emotional/behavioral difficulties (SDQ). Associations were assessed using chi-square tests, Kendall’s tau correlations, and two-factor ANOVA models. Results: Elevated depressive symptoms were present in 10.7% of adolescents, emotional/behavioral difficulties in 13.9%, and anxiety symptoms in 27.9% of adolescents. Female adolescents consistently showed higher symptom levels in all domains. Self-reported financial burden was the strongest and most consistent correlate of mental health problems, demonstrating small-to-moderate positive correlations with depressive symptoms (τ = 0.20, p < 0.001), emotional/behavioral difficulties (τ = 0.14, p < 0.001), and anxiety (τ = 0.25, p < 0.001). Parent-reported burden showed weaker and less consistent associations, and the Family Affluence Scale III was not significantly related to any of the mental health outcomes. ANOVA models indicated that adolescents’ own perception of financial burden significantly predicted anxiety levels in both age groups (11–14 and 15–19 years), whereas discrepancies between adolescent and parent burden perceptions were particularly relevant among younger adolescents. Conclusions: In this affluent European region, subjective financial strain, especially adolescents’ perception of burden due to rising prices, is a stronger determinant of depressive symptoms, anxiety, and psychosocial difficulties than parental burden reports or structural affluence indicators. Adolescents, especially females, appear to be particularly vulnerable. These findings underscore the importance of addressing subjective financial stress in adolescent mental health and public health strategies. Full article

The integration of flexible electronics and machine learning (ML) algorithms has become a revolutionary force driving the field of intelligent sensing, giving rise to a new generation of intelligent devices and systems. This article provides a systematic review of core technologies and practical applications of ML in flexible electronics. It focuses on analyzing the theoretical frameworks of algorithms such as the Long Short-Term Memory Network (LSTM), Convolutional Neural Network (CNN), and Reinforcement Learning (RL) in the intelligent processing of sensor signals (IPSS), multimodal feature extraction (MFE), process defect and anomaly detection (PDAD), and data compression and edge computing (DCEC). This study explores the performance advantages of these technologies in optimizing signal analysis accuracy, compensating for interference in high-noise environments, optimizing manufacturing process parameters, etc., and empirically analyzes their potential applications in wearable health monitoring systems, intelligent control of soft robots, performance optimization of self-powered devices, and intelligent perception of epidermal electronic systems. Full article

Objectives: This pilot study examined telerehabilitation, which has emerged as a crucial modality in light of recent global challenges such as the COVID-19 pandemic. We examined the effectiveness of a mobile health telerehabilitation intervention developed for older adults with frailty. Methods: Six participants received a telerehabilitation intervention (Rehab Studio) that included exercise training videos. The participants were aged ≥65 years, had no history of dementia or psychiatric disorders, and had mild-to-moderate care needs. For 1 month, the participants received 1 h live online rehabilitation sessions with real-time communication with rehabilitation specialists (physical therapists and occupational therapists: PTs/OTs). The quality of life (QoL) (EuroQol 5 dimensions 5-level [EQ-5D-5L] questionnaire) and self-rated health scores were recorded before and after the intervention, and the data were analyzed using paired t-tests to determine whether the service was effective. Results: Significant differences were found in the total EQ-5D-5L and self-rated health scores (p < 0.05). The mean EQ-5D-5L score increased from 0.63 ± 0.13 before the intervention to 0.77 ± 0.14 after the intervention (p = 0.010), while the mean self-rated health score increased from 66.0 ± 18.0 to 83.3 ± 10.3, respectively (p = 0.019). Conclusions: This study revealed that the mobile health telerehabilitation intervention is safe and can improve QoL for older adults with frailty. However, the effectiveness of the intervention needs to be further investigated in patients with poor performance in daily living activities. Telerehabilitation could help to reduce the burden of nursing care in aging societies with declining birthrates. However, given the extremely small sample size (N = 6), these p-values should be interpreted with considerable caution. Statistical significance in such a small sample does not provide strong evidence for population-level effects, and our findings should be regarded as hypothesis-generating rather than confirmatory. Full article

Objectives: This cross-sectional study aimed to evaluate oral health knowledge, self-perceived oral status, hygiene behaviors, prevalence of dental injuries, and mouthguard use among Croatian basketball players and coaches. Materials and Methods: A total of 414 participants of both sexes, spanning various levels of basketball participation in Croatia, completed an anonymous online questionnaire assessing oral health knowledge, hygiene habits, trauma history, and preventive practices. Data was analyzed using descriptive statistics, Chi-square, Mann–Whitney, and Kruskal–Wallis tests. Results: Participants demonstrated generally poor oral health knowledge, with coaches scoring significantly higher than players (p < 0.05), and knowledge levels varying according to education and socioeconomic status (p ≤ 0.001). A majority reported no prior dental trauma (69.6%), and mouthguard use was notably low (20.8%). While regular toothbrushing was prevalent (87%), the use of dental floss was limited (39.9%). The most reported oral health problems included gingival bleeding (37.9%), dental calculus (35.0%), and tooth sensitivity (34.3%). Conclusions: Despite relatively good hygiene habits, Croatian basketball players demonstrated low oral health knowledge and insufficient preventive practices, particularly regarding mouthguard use. These findings indicate the need for targeted education and preventive strategies within the basketball community. Full article

Background: Dietary habits established during young adulthood have long-term implications for health, and food choices among university students are strongly shaped by contextual factors. Institutional eating environments represent a relevant setting for promoting healthier dietary behaviors, yet limited evidence integrates students’ engagement with these settings, their food consumption patterns across contexts, and the individual decision-making processes underlying food choice. Methods: This cross-sectional study analyzed survey data from 1519 students enrolled at a large Italian university. Measures included sociodemographic characteristics, self-identified dietary style, engagement with the university canteen, consumption frequency of selected food categories across institutional and non-institutional contexts, and category-specific food-choice motivations. Data were analyzed using descriptive analyses, Borda count rankings, paired comparisons, and multiple linear regression models. Results: Clear contextual differences in food consumption emerged across all food categories, with consistently lower consumption frequencies within the university canteen compared to outside settings (all p < 0.001). The largest contextual gap was observed for fruit consumption (d = 0.94), with similarly pronounced differences for plant-based foods. Taste was the most salient decision-making factor across food categories (overall M ≈ 4.4), while health-related motives were more prominent for healthier foods and gratification for desserts. Across contexts, self-identified dietary style was the most consistent predictor of food consumption, explaining substantial variance for animal-based protein consumption (R² = 0.293 in the canteen; R² = 0.353 outside), whereas age and gender showed smaller, food-specific associations. Conclusions: The findings highlight institutional eating settings as distinct food environments in which individual dietary preferences are only partially expressed. Effective strategies to promote healthier eating among university students should move beyond generic approaches and integrate interventions targeting service-related engagement, category-specific choice architecture, and students’ dietary identities. Full article

Medical students experience fluctuations in their motivation, influenced by various factors, including curricular rigor, mental health, and institutional factors. Based on Self-Determination Theory (SDT) and the Four Pillars of Academic Engagement (HPEE), this study, conducted at a private Mexican university, examined motivational variation according to academic year, curricular impact, gender differences, and its relationship with mental health. Methods: A quantitative, cross-sectional, descriptive study was conducted using qualitative tools for contextualization (n = 1326). Mann–Whitney U tests, Kruskal–Wallis tests, logistic regression, and psychological network analysis were performed. Results: Motivation showed cross-sectional variation: high in preclinical years 1 and 2, decreasing in clinical years 3 and 4 (p < 0.001), and rebounding in year 6. The reformed curriculum (elective subjects, student-centered active learning) resulted in greater motivation (OR = 10.68, p < 0.001). Women tended to have slightly higher motivation (p = 0.050), higher grade point averages (p < 0.001), but also greater stress (p < 0.001). Network analysis revealed that intrinsic achievement (centrality = 1.11) and curiosity about knowledge (predictability = 84.5%) are the main drivers, while demotivation was linked to the later years. The qualitative part of the study showed altruism/curiosity as the main motivators; mistreatment/workload (demotivators). Conclusions: Motivation is context-sensitive, peaks in the preclinical stage, and recovers with autonomy but is vulnerable during clinical immersion. Autonomy in course selection, active student-centered pedagogies, and gender-sensitive support foster sustained participation. The centrality of intrinsic factors in the network highlights that achievement motivation and knowledge are general and independent motivators. Qualitative data reveal systemic barriers. Stage-specific interventions, such as mentoring, student support programs, and reporting mistreatment, can be crucial for strengthening resilience and performance. Longitudinal and multi-institutional studies are needed to validate the causality and generalizability of this study. Full article

The dynamic growth of the electrified vehicle (xEV) market, including both electric and hybrid vehicles, has increased the demand for advanced Battery Management Systems (BMS). From an energy-systems perspective, xEV batteries act as distributed energy storage units that strongly interact with power grids, renewable generation, and charging infrastructure, making their efficient control a key element of low-carbon energy systems. Traditional BMS methods face challenges in accurately estimating key battery states and parameters, especially under dynamic operating conditions. This review systematically analyzes the progress in applying artificial intelligence, machine learning, and other advanced computational and data-driven algorithms to improve the performance of xEV battery management with a particular focus on energy efficiency, safe utilization of stored electrochemical energy, and the interaction between vehicles and the power system. The literature analysis covers key research trends from 2020 to 2025. This review covers a wide range of applications, including State of Charge (SOC) estimation, State of Health (SOH) prediction, and thermal management. We examine the use of various methods, such as deep learning, neural networks, genetic algorithms, regression, and also filtering algorithms, to solve these complex problems. This review also classifies the research by geographical distribution and document types, providing insight into the global landscape of this rapidly evolving field. By explicitly linking BMS functions with energy-system indicators such as charging load profiles, peak-load reduction, self-consumption of photovoltaic generation, and lifetime-aware energy use, this synthesis of contemporary research serves as a valuable resource for scientists and engineers who wish to understand the latest achievements and future directions in data-driven battery management and its role in modern energy systems. Full article

Background: Health literacy is increasingly recognized as an essential determinant of health, readiness, and safety in the military, especially as health systems become more digitalized. However, evidence on general and digital health literacy in the armed forces remains limited. This study examined levels of general health literacy and digital health literacy among Lithuanian soldiers and explored their associations with sociodemographic, service-related, and health characteristics. Methods: A cross-sectional survey was conducted among 603 military personnel serving in the national armed forces. General and digital health literacy were measured with HLS19-Q12 and HLS19-DIGI. Data on sociodemographic and military characteristics, self-rated health, and self-reported long-term illnesses were collected. Descriptive statistics, correlation analyses, and multivariable regression models were used to analyze the data. Results: The sample was predominantly male (81.9%) with a mean age of 39.08 years (SD = 8.89). The mean general health literacy score was 80.1 (SD = 19.17), whereas the mean digital health literacy score was 67.81 (SD = 30.05). Overall, 45.0% of soldiers had excellent general health literacy, and 12.0% had inadequate general health literacy; 42.1% had excellent digital health literacy, and 35% had inadequate digital health literacy. Higher levels of health literacy were positively associated with better self-rated health and social status. No statistically significant associations were found between health literacy and gender, age, education, length of service, type of military service, and self-reported long-term health complaints. Conclusions: Military personnel in this study displayed relatively high general health literacy, yet digital health literacy was lower and more unevenly distributed, indicating a potential vulnerability for health outcomes as access to information, communication, and care increasingly relies on digital platforms. Given the cross-sectional design, causal inferences cannot be drawn. Military health services may build on existing health literacy strengths while considering strategies to address digital health literacy gaps (e.g., targeted training, tailored support, and user-friendly digital solutions, including service design), acknowledging that feasibility and implementation depend on organizational context and resources. Full article

Background: Depression in older adults is a multifactorial condition influenced by health status, functional capacity, and lifestyle factors. This study aimed to investigate the individual and combined associations of these factors with late-life depression. Methods: Using data from the National Health and Nutrition Examination Survey (NHANES), this study evaluated the associations of general health, chronic conditions, functioning, and lifestyle behaviors (including physical activity, sleep, diet quality, smoking, and alcohol use) with depressive symptoms among U.S. adults 65 years and older. Weighted logistic regression models, accounting for the complex survey design of NHANES, were used to examine the factors both individually and in combination. Results: Depressive symptoms were more prevalent among individuals with poor self-rated health, physical and cognitive functional limitations, hypertension, obesity, current smoking, physical inactivity, and alcohol abstinence. A clear cumulative risk gradient was observed with increasing numbers of risk factors: older adults with six or more risk factors had at least 20-fold higher likelihood of depressive symptoms compared with those with one or no risk factors. Conclusions: These findings highlight the interdependent influences of health, function, and lifestyle on late-life depressive symptoms and underscore the need for integrative prevention and intervention strategies to promote mental well-being in aging populations. Full article

Cyberbullying generally reveals two leading players: the attacker side (perpetrator) and the victim side; each side has its distinctive social and psychological dynamics. In most prior empirical studies, the victim side is pivotal, as it bears the direct psychological and emotional consequences of online aggression. Recently, cyberbullying victimisation has been elevated as a main psychological concern among university students. Nevertheless, the moderating role of family support remained untested, particularly in a collectivist cultural context such as the Kingdom of Saudi Arabia (KSA). This study tested the impacts of cyberbullying victimisation on mental health consequences (anxiety, stress, and depression) among KSA university students. The study further tested family support as a moderator in these relationships. Data was collected from 650 students employing a self-structured survey. The data obtained was analyzed using “Partial Least Squares Structural Equation Modelling” (PLS-SEM). The findings revealed that cyberbullying-victimization can significantly raise students’ anxiety, stress, and depressive signs, supporting its place as a critical psychological risk factor. Contrary to the “traditional stress-buffering theory”, family support failed to alleviate the influence of cyberbullying-victimization on anxiety and stress, and unexpectedly, higher levels of family support were related to higher depressive levels, suggesting a reverse-buffering impact. These results highlighted the complicated relationships between family support and emotional outcomes in the context of digital threats. The study stressed the urgent need for culturally delicate mediations, such as training sessions for digital resilience, and colleague-based supportive systems to successfully deal with the mental health consequences of cybervictimization. Full article

First Nations children remain dramatically over-represented in Australia’s Out-of-Home Care (OOHC) system, particularly in New South Wales (NSW), which continues to report the highest numbers nationally. This narrative review, grounded in a relational First Nations Standpoint Theory and decolonising research paradigms, to critically examine the systemic, structural, and historical factors contributing to these disproportionalities. Drawing on interdisciplinary evidence across law, criminology, education, health, governance studies, and public policy, the analysis centres Indigenous-authored scholarship and contemporary empirical literature, including grey literature, inquiries, and community-led reports. Findings reveal that the OOHC system reproduces the colonial logics that historically drove the Stolen Generations. Macro-level structural drivers—including systemic racism, Indigenous data injustice, entrenched poverty and deprivation, intergenerational trauma, and Westernised governance frameworks—continue to shape child protection policies and practices. Micro-level drivers such as parental supports, mental health distress, substance misuse, family violence, and the criminalisation of children in care (“crossover children”) must be understood as direct consequences of structural inequality rather than as isolated individual risk factors. Current placement and permanency orders in NSW further compound cultural disconnection, with ongoing failures to implement the Aboriginal and Torres Strait Islander Child Placement Principle (ATSICPP). Contemporary cultural rights and Indigenous Cultural and Intellectual Property (ICIP) frameworks highlight the urgency of restoring Indigenous authority in decision-making processes. The literature consistently demonstrates that cultural continuity, kinship networks, and ACCO-led models are sort to produce stronger long-term outcomes for children. The review concludes that genuine transformation requires a systemic shift toward Indigenous-led governance, community-controlled service delivery, data sovereignty, and legislative reform that embeds cultural rights and self-determination. Without acknowledging the structural drivers and redistributing genuine power and authority, the state risks perpetuating a cycle of removal that mirrors earlier assimilationist policies. Strengthening First Peoples governance and cultural authority is therefore essential to creating pathways for First Nations children to live safely, remain connected to family and kin, and thrive in culture. Full article

Crack type classification in concrete structures is essential for assessing structural integrity, yet traditional visual inspections and RA–AF parameter-based Acoustic Emission (AE) methods suffer from subjectivity and limited ability to capture temporal signal dependencies. This study proposes TemporalAE-Net, a self-attention-based machine learning framework designed to classify tensile and shear cracks while explicitly incorporating the temporal evolution of AE signals. AE data were collected from axial tension tests, shear-failure tests, and four-point bending tests on reinforced concrete beams, and a sliding-window reconstruction method was used to transform sequential AE signals into two-dimensional temporal matrices. TemporalAE-Net integrates one-dimensional convolution for local feature extraction and multi-head self-attention for global temporal correlation learning, followed by multilayer perceptron classification. The proposed model achieved an accuracy of 99.72%, outperforming both its ablated variants without convolutional or attention modules and conventional time-series architectures. Generalization tests on 12 unseen specimens yielded 100% correct classifications, and predictions for reinforced concrete beams closely matched established crack-evolution patterns, with shear cracks detected approximately 15 s prior to visual observation. These results demonstrate that TemporalAE-Net effectively captures temporal dependencies in AE signals. Moreover, it provides accurate and efficient tensile–shear crack identification, making it suitable for real-time structural health monitoring applications. Full article